#coding=utf-8
from datetime import datetime
import math
import time
import tensorflow as tf
#设置batch_size=32，num_batches为100
batch_size = 32
num_batches = 100
#定义一个现实网络每一层结构的函数print_actications,展示每一个卷积层或池化层输出tensor的尺寸。
def print_activations(t):
    print(t.op.name, ' ', t.get_shape().as_list())
#设计Alexnet的网络结构
def inference(images):
    parameters = []
    with tf.name_scope('conv1') as scope:
        #第一个卷积层
        kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32, stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name='bias')
        bias = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(bias, name=scope)
        print_activations(conv1)
        parameters += [kernel, biases]
    #添加LRN和最大池化层
    lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001/9, beta=0.75, name='lrn1')
    pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool1')
    print_activations(pool1)
    #第二个卷积层
    with tf.name_scope('conv2') as scope:
        kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192], dtype=tf.float32, stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[192], dtype=tf.float32), trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(bias, name=scope)
        parameters += [kernel, biases]
        print_activations(conv2)
    #对conv2处理
    lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9, beta=0.75, name='lrn2')
    pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool2')
    print_activations(pool2)
    #第3个卷积层
    with tf.name_scope('conv3') as scope:
        kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384], dtype=tf.float32, stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32), trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv3 = tf.nn.relu(bias, name=scope)
        parameters += [kernel, biases]
        print_activations(conv3)
    #第4个卷积层
    with tf.name_scope('conv4') as scope:
        kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256], dtype=tf.float32, stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv4 = tf.nn.relu(bias, name=scope)
        parameters += [kernel, biases]
        print_activations(conv4)
    #第5个卷积层
    with tf.name_scope('conv5') as scope:
        kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32, stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv5 = tf.nn.relu(bias, name=scope)
        parameters += [kernel, biases]
        print_activations(conv5)
    #最大池化层pool5
    pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool5')
    print_activations(pool5)
    #未添加全连接层，因为对计算耗时影响小
    return pool5, parameters

#定义AlexNet的每轮时间评估函数
def time_tensorflow_run(session, target, info_string):
    num_steps_burn_in = 10 #程序预热
    total_durations = 0.0
    total_duration_squared = 0.0
    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print('%s: step %d, duration = %.3f'%(datetime.now(), i - num_steps_burn_in, duration))
            total_durations += duration
            total_duration_squared += duration * duration
    #计算每轮迭代的平均耗时和标准差sd,最后将结果显示出来
    mn = total_durations / num_batches
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)
    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch'%(datetime.now(), info_string, num_batches, mn, sd))
#定义主函数run_benchmark
def run_benchmark():
    with tf.Graph().as_default():
        image_size = 224
        images = tf.Variable(tf.random_normal([batch_size, image_size, image_size, 3], dtype=tf.float32, stddev=1e-1))
        pool5, parameters = inference(images)
        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)
        time_tensorflow_run(sess, pool5, "Foward")
        objective = tf.nn.l2_loss(pool5)
        grad = tf.gradients(objective, parameters)
        time_tensorflow_run(sess, grad, "Foward-backward")

run_benchmark()

